Parametric inference of recombination in HIV genomes

TL;DR

This paper introduces a hidden Markov model with parametric inference for detecting and annotating recombinant HIV genomes, improving accuracy over existing methods and enabling comprehensive analysis of large datasets.

Contribution

It presents a novel hidden Markov model framework with parametric inference for identifying recombinant HIV sequences and their parental origins, enhancing detection accuracy.

Findings

Recover most features of hand-curated annotations

Feasible for full-length HIV genomes

Improves detection and annotation of recombinants

Abstract

Recombination is an important event in the evolution of HIV. It affects the global spread of the pandemic as well as evolutionary escape from host immune response and from drug therapy within single patients. Comprehensive computational methods are needed for detecting recombinant sequences in large databases, and for inferring the parental sequences. We present a hidden Markov model to annotate a query sequence as a recombinant of a given set of aligned sequences. Parametric inference is used to determine all optimal annotations for all parameters of the model. We show that the inferred annotations recover most features of established hand-curated annotations. Thus, parametric analysis of the hidden Markov model is feasible for HIV full-length genomes, and it improves the detection and annotation of recombinant forms. All computational results, reference alignments, and C++ source code are available at http://bio.math.berkeley.edu/recombination/.